In this paper, we introduce a novel cost aggregation network, dubbed Volumetric Aggregation with Transformers (VAT), to tackle the few-shot segmentation task by using both convolutions and transformers to efficiently handle high dimensional correlation maps between query and support. Specifically, within our encoder, we propose volumetric embedding module consisting of a series of 4D convolutions to not only transform high-dimensional correlation maps into more tractable size, but also inject some convolutional bias to aid the subsequent processing by transformers [1]}. The output then undergoes volumetric transformer module for the cost aggregation with 4D swin transformer. With these combined, our encoder processes the input in a pyramidal manner to expedite the learning by letting the aggregated correlation maps at coarser level play as a guidance to finer level, enforcing to learn complementary matching scores. Subsequent to encoder, our affinity-aware decoder refines the aggregated costs with the help from appearance affinity and makes a prediction.
